What Is Propensity Scoring in a CDP and How Does It Work?

What is Propensity Scoring

Propensity scoring is a method that uses statistical modeling and machine learning to estimate the likelihood that a specific user will take a desired action, such as completing a conversion. In Sitefinity Insight, it helps identify which visitors are more likely to convert based on their behavior and similarity to past converters. Typical use cases include prioritizing leads for sales outreach, targeting high-probability users with personalized content and optimizing campaigns for better return on investment.

How does Sitefinity Insight calculate propensity scores for customer actions?

Sitefinity Insight uses AI and machine learning algorithms to model the behavior of anonymous visitors and known contacts compared to those who have already converted. The model classifies visitors and contacts into two groups based on their probability of conversion: high and medium.

Can we customize the factors that influence propensity scoring models?

No, the feature is self-sufficient and does not require additional configuration.

How often are propensity scores updated based on new data?

Propensity scores are updated once a day.

How do scoring models work for leads, accounts or visitors?

You can define unlimited models with positive/negative scoring, resets and disqualification rules; each funnel step transition can be acted on independently.